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submission 545462

rajesh0042 · python · License unknown
Kernel source · 11 lines ↓

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Vendorable · source mirrored · license unknownView source →

No package. Vendor the mirrored source: 11 lines, June 9 Researcher Reciprocity License v1.0.

vectoradd_v2.py
curl "https://kernelindex.com/api/v1/implementations/kernelbot-vectoradd-v2-545462?include=source"
interfacepython
Compatibility
measured onNVIDIA A100
declared hardwareNVIDIA A100
architecturessm_80
dtypesfp16

Benchmark evidence

1 measurement across 1 GPU, fastest first.

Operation / workload
Hardware
Latency
Rank
Observed
FP16 vector additionsuite of 5 cases
NVIDIA A100
895.3µs
#8= of 87
2026-03-13
Run →

Reported · How evidence levels are derived →

Source and license

sourceavailable
revision digestsha256:24dfc3c3e91e8a5f2948fc6cacd1043c06e7117be646b1a97532d21453d3ef3d
license declaredunknown
license concludedunknown
authorsrajesh0042
imported2026-08-15

Kernel source

vectoradd_v2.py11 lines
import os
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"

import torch
from task import input_t, output_t

def custom_kernel(data: input_t) -> output_t:
    A, B, output = data
    torch.add(A, B, out=output)
    return output

Source code from GPU Mode and the KernelBot dataset · June 9 Researcher Reciprocity License v1.0

Changes from previous submission

Against this author's previous submission submission 545319.

  ⋯ 3 unchanged lines
  import torch
  from task import input_t, output_t
   
- # VectorAdd with warmup to amortize kernel launch overhead
- # The gap is only 3µs -- every microsecond counts
-  
- # Warm up CUDA context and kernel caches
- def _warmup():
-     for size in [1024, 4096, 16384, 65536, 262144, 1048576]:
-         a = torch.randn(size, device='cuda', dtype=torch.float16)
-         b = torch.randn(size, device='cuda', dtype=torch.float16)
-         c = torch.empty(size, device='cuda', dtype=torch.float16)
-         torch.add(a, b, out=c)
-         torch.add(a, b, out=c)
-     torch.cuda.synchronize()
-  
- _warmup()
-  
  def custom_kernel(data: input_t) -> output_t:
      A, B, output = data
      torch.add(A, B, out=output)

Best evidence level for this revision: reported

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